Previous studies mainly focus on learning when and whom to share local observations, improving coordination compared to independent learning [1]}, [2]}, [3]}.
However, the learning suffers from the environment non-stationarity, which is caused by the changing of agents’ policies during learning
procedure [4]}.
Modelling others has been put forward to alleviate the non-stationarity by predicting others' behaviours or goals [5]}, [6]}.
From the perspective of communication, agents can benefit from intention sharing and know others' intentions directly instead of inference [7]}, [8]}.
However, the agents may change their actions simultaneously after intention sharing, resulting in message deceiving, which is defined as the mismatch between
the propagated intentions and the final decisions.
Message deceiving could degrade coordination and impair learning stability.
For instance, two agents arrive at a crossroad where both of them send their intentions “wait” to the other. After knowing that the other would make way,
they simultaneously change their actions from “wait” to “move”.
In this case, agents are misled into making wrong decisions based on deceptive messages, resulting in a collision.
A simple yet effective method to address message deceiving is one-sided intention sharing, i.e., only one agent has access to the other plans to do [9]},
which ensures that the intention from the agent is consistent with its final decision.
